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Abstract：
The spatial and temporal characteristics of fine particulate matter (PM2.5, particulate matter <2.5 μm in aerodynamic diameter) are increasingly being studied from satellite aerosol remote sensing data. However, cloud cover severely limits the coverage of satellite-driven PM2.5 models, and little research has been conducted on the association between cloud properties and PM2.5 levels. In this study, we analyzed the relationships between ground PM2.5 concentrations and two satellite-retrieved cloud parameters using data from the Southeastern Aerosol Research and Characterization (SEARCH) Network during 2000-2010. We found that both satellite-retrieved cloud fraction (CF) and cloud optical thickness (COT) are negatively associated with PM2.5 levels. PM2.5 speciation and meteorological analysis suggested that the main reason for these negative relationships might be the decreased secondary particle generation. Stratified analyses by season, land use type, and site location showed that seasonal impacts on this relationship are significant. These associations do not vary substantially between urban and rural sites or inland and coastal sites. The statistically significant negative associations of PM2.5 mass concentrations with CF and COT suggest that satellite-retrieved cloud parameters have the potential to serve as predictors to fill the data gap left by satellite aerosol optical depth in satellite-driven PM2.5 models.
摘要：
从卫星气溶胶遥感数据中越来越多地研究细颗粒物（PM2.5，颗粒物的空气动力学直径<2.5μm）的时空特征。然而,云层严重限制了卫星驱动的PM2.5模型的覆盖范围，关于云属性与PM2.5水平之间的关联的研究很少。在这项研究中,我们使用东南气溶胶研究和表征(SEARCH)网络在2000-2010年间的数据,分析了地面PM2.5浓度与两个卫星检索的云参数之间的关系.我们发现，卫星检索的云分数（CF）和云光学厚度（COT）与PM2.5水平呈负相关。PM2.5形态和气象分析表明，这些负相关的主要原因可能是二次粒子生成减少。按季节进行分层分析，土地利用类型,和站点位置表明，季节性对这种关系的影响是显著的。这些协会在城市和农村地区或内陆和沿海地区之间没有很大差异。PM2.5质量浓度与CF和COT的统计显着负相关表明，卫星检索的云参数有可能作为预测因子，以填补卫星驱动的PM2.5模型中卫星气溶胶光学深度留下的数据空白。
